Xu, Mingzhe and Abdullah, Nor Aniza and Md Sabri, Aznul Qalid (2024) A method to improve the prediction performance of cancer-gene association by screening negative training samples through gene network data. Computational Biology and Chemistry, 108. ISSN 1476-9271, DOI https://doi.org/10.1016/j.compbiolchem.2023.107997.
Full text not available from this repository.Abstract
This work focuses on data sampling in cancer-gene association prediction. Currently, researchers are using machine learning methods to predict genes that are more likely to produce cancer-causing mutations. To improve the performance of machine learning models, methods have been proposed, one of which is to improve the quality of the training data. Existing methods focus mainly on positive data, i.e. cancer driver genes, for screening selection. This paper proposes a low-cancer-related gene screening method based on gene network and graph theory algorithms to improve the negative samples selection. Genetic data with low cancer correlation is used as negative training samples. After experimental verification, using the negative samples screened by this method to train the cancer gene classification model can improve prediction performance. The biggest advantage of this method is that it can be easily combined with other methods that focus on enhancing the quality of positive training samples. It has been demonstrated that significant improvement is achieved by combining this method with three state-of-the-arts cancer gene prediction methods. © 2023 Elsevier Ltd
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Bioinformatics; Cancer gene classification; Gene network; Machine learning; Preprocessing; System biology |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 02 Jul 2024 05:05 |
| Last Modified: | 02 Jul 2024 05:05 |
| URI: | http://eprints.um.edu.my/id/eprint/44823 |
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